{% extends "pathways/base.html" %}
{% load static %}
{% load i18n %}
{% block content %}
{% trans "Eligible Buffalo residents can receive a discount on their water bill between $60 and $90 a year." %}
{% trans "An official Affordable Water Program assistance tool" %}
{% trans "Upload requested documents" %} {% trans "For other issues" %} {% trans "Apply in 10 minutes" %} {% trans "Eligibility is determined by income and household size." %} {% trans "Verify documents" %} {% trans "You'll need documents to verify your income and residency." %} {% trans "Get discount on water" %} {% trans "If you’re approved, you’ll receive a discount on your next water bill." %} You may qualify for additional Pathways to Affordable Water. These include {% trans "Yes! Though it may depend on who pays the water bill." %}
{% blocktrans trimmed %}
If you pay your own water bill, you may apply normally. If you do not pay your own water bill or if water is included in your rent, you might
still be eligible for water assistance.
{% endblocktrans %}
{% blocktrans trimmed %}
It is possible for your landlord to receive the water discount on your behalf.
However, this is not legal advice and we highly recommend
reviewing the Tenant's Rights Guide prepared by the office of the New York State Attorney General.
{% endblocktrans %}
{% trans "Get help paying your Buffalo Water bill" %}
{% trans "Apply for assistance in 10 minutes" %}
{% trans "Apply now" %}
{% trans "ALREADY STARTED YOUR APPLICATION?" %}
{% trans "What if I need additional help?" %}
{% trans "Can I apply as a renter?" %}


These constants define properties common to all material types, with the
exception of Texture Combine Operations which only apply to
[page:MeshBasicMaterial.combine MeshBasicMaterial],
[page:MeshLambertMaterial.combine MeshLambertMaterial] and
[page:MeshPhongMaterial.combine MeshPhongMaterial].
THREE.FrontSide
THREE.BackSide
THREE.DoubleSide
Defines which side of faces will be rendered - front, back or both. Default is [page:Constant FrontSide].
THREE.NoBlending
THREE.NormalBlending
THREE.AdditiveBlending
THREE.SubtractiveBlending
THREE.MultiplyBlending
THREE.CustomBlending
These control the source and destination blending equations for the
material's RGB and Alpha sent to the WebGLRenderer for use by WebGL.
[page:Constant NormalBlending] is the default.
Note that [page:Constant CustomBlending] must be set to use
[page:CustomBlendingEquation Custom Blending Equations].
See the [example:webgl_materials_blending materials / blending]
example.
THREE.NeverDepth
THREE.AlwaysDepth
THREE.EqualDepth
THREE.LessDepth
THREE.LessEqualDepth
THREE.GreaterEqualDepth
THREE.GreaterDepth
THREE.NotEqualDepth
Which depth function the material uses to compare incoming pixels Z-depth
against the current Z-depth buffer value. If the result of the comparison
is true, the pixel will be drawn.
[page:Materials NeverDepth] will never return true.
[page:Materials AlwaysDepth] will always return true.
[page:Materials EqualDepth] will return true if the incoming pixel Z-depth
is equal to the current buffer Z-depth.
[page:Materials LessDepth] will return true if the incoming pixel Z-depth
is less than the current buffer Z-depth.
[page:Materials LessEqualDepth] is the default and will return true if the
incoming pixel Z-depth is less than or equal to the current buffer
Z-depth.
[page:Materials GreaterEqualDepth] will return true if the incoming pixel
Z-depth is greater than or equal to the current buffer Z-depth.
[page:Materials GreaterDepth] will return true if the incoming pixel
Z-depth is greater than the current buffer Z-depth.
[page:Materials NotEqualDepth] will return true if the incoming pixel
Z-depth is not equal to the current buffer Z-depth.
THREE.MultiplyOperation
THREE.MixOperation
THREE.AddOperation
These define how the result of the surface's color is combined with the
environment map (if present), for [page:MeshBasicMaterial.combine MeshBasicMaterial],
[page:MeshLambertMaterial.combine MeshLambertMaterial]
and [page:MeshPhongMaterial.combine MeshPhongMaterial].
[page:Constant MultiplyOperation] is the default and multiplies the
environment map color with the surface color.
[page:Constant MixOperation] uses reflectivity to blend between the two
colors.
[page:Constant AddOperation] adds the two colors.
THREE.NeverStencilFunc
THREE.LessStencilFunc
THREE.EqualStencilFunc
THREE.LessEqualStencilFunc
THREE.GreaterStencilFunc
THREE.NotEqualStencilFunc
THREE.GreaterEqualStencilFunc
THREE.AlwaysStencilFunc
Which stencil function the material uses to determine whether or not to
perform a stencil operation.
[page:Materials NeverStencilFunc] will never return true.
[page:Materials LessStencilFunc] will return true if the stencil reference
value is less than the current stencil value.
[page:Materials EqualStencilFunc] will return true if the stencil
reference value is equal to the current stencil value.
[page:Materials LessEqualStencilFunc] will return true if the stencil
reference value is less than or equal to the current stencil value.
[page:Materials GreaterStencilFunc] will return true if the stencil
reference value is greater than the current stencil value.
[page:Materials NotEqualStencilFunc] will return true if the stencil
reference value is not equal to the current stencil value.
[page:Materials GreaterEqualStencilFunc] will return true if the stencil
reference value is greater than or equal to the current stencil value.
[page:Materials AlwaysStencilFunc] will always return true.
THREE.ZeroStencilOp
THREE.KeepStencilOp
THREE.ReplaceStencilOp
THREE.IncrementStencilOp
THREE.DecrementStencilOp
THREE.IncrementWrapStencilOp
THREE.DecrementWrapStencilOp
THREE.InvertStencilOp
Which stencil operation the material will perform on the stencil buffer
pixel if the provided stencil function passes.
[page:Materials ZeroStencilOp] will set the stencil value to `0`.
[page:Materials KeepStencilOp] will not change the current stencil
value.
[page:Materials ReplaceStencilOp] will replace the stencil value with the
specified stencil reference value.
[page:Materials IncrementStencilOp] will increment the current stencil
value by `1`.
[page:Materials DecrementStencilOp] will decrement the current stencil
value by `1`.
[page:Materials IncrementWrapStencilOp] will increment the current stencil
value by `1`. If the value increments past `255` it will be set to `0`.
[page:Materials DecrementWrapStencilOp] will increment the current stencil
value by `1`. If the value decrements below `0` it will be set to
`255`.
[page:Materials InvertStencilOp] will perform a bitwise inversion of the
current stencil value.
THREE.TangentSpaceNormalMap
THREE.ObjectSpaceNormalMap
Defines the type of the normal map. For TangentSpaceNormalMap, the information is relative to the underlying surface. For ObjectSpaceNormalMap, the information is relative to the object orientation. Default is [page:Constant TangentSpaceNormalMap].
THREE.GLSL1
THREE.GLSL3
[link:https://github.com/mrdoob/three.js/blob/master/src/constants.js src/constants.js]


IFU
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Network Analysis Algorithms for Disease Gene Prioritization
Biological research has made many advances in the last 50 years. We now have incredible amounts of data and no good way to use it to its full potential. This data takes the form of protein-protein interaction networks which are large graphs where nodes are proteins and edges represent an interaction between two proteins. Our research is on how graph analysis algorithms can be used to help biologists more efficiently research genetic diseases.
We implemented and explored the performance of 3 different algorithms, Page Rank, Random Walk with Restart, and Diffusion Kernel, on different diseases to try and predict previously unstudied candidate genes that might be related to these diseases. These algorithms were originally developed and used for very different purposes than computational biology. PageRank was developed by Google to rank webpages, Random Walk with Restart was designed for image segmentation, and Diffusion Kernel was designed to model heat flow. However, we can take these algorithms designed for more abstract tasks and apply them to the biological sciences. We used a large Protein-Protein interaction network with 20,000 nodes and 12 million interactions and sets of known disease genes to predict candidate genes for research for ischemic stroke, endometriosis, and lymphoma. In our presentation, we will discuss the biological data we used, the performance of the algorithms and their sensitivities to graph shape, and novel genes we predicted.


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To add server side oprations in data grid, set isServerOperations to "true" in grid defination.
And also add method onLoadData(options:LoadOptions). This method will be invoked every time to fetch data.
LoadOptions has below properties:
{{ '{' }} column : string, sortDirection: string {{'}'}})
{{ '{' }} column : string, filterOperator: string, filterValue: string, dataType: string {{'}'}})
Possible values for sortDirection are asc, desc.
Possible values for filterOperator are startswith, endswith, contains, gt, lt, ge, le, eq, ne.
Possible values for dataType are number, string, date, boolean.
pass data to dataSource with "next()" method of dataSource as below
options:{{currentLoadOptions | json}}


Biographical Sketch of Elmer
Dellwood Ellsworth McCurdy
This biosketch of Elmer was taken from the writings
of Dr. Charles W. McCurdy of
Philadelphia in the early 1900's.
Elmer Dellwood Ellsworth McCurdy (son of
Aaron McCurdy and Rebecca Kinkade) was born near Marietta, Ohio April 10,
1863. As he grew into boyhood he became
sadly afflicted with epileptic fits which deprived him from attending school
regular; therefore, he received a very limited education.
His mother dying in 1879, he left home a
year later and went to Atlantic City, NJ and there became man-of-all-work with
Lee s Ethoscope Combination. He
travelled with him through the south.
In 1882, he became Stage Manager of the Gem Theatre in Baltimore, and in
1883 of Winters Summer Garden. In 1884
he became Assistant Advance Agent for Teddy O Neil s Circus; and quit the show
business in the spring of 1885. He
entered it again in 1887 at Beaver Falls, PA touring with John McCready s Stock
Company through the east and Canada.
He married first Rebecca J. Bradin of
Jackson County, WV, to which one son was born: Harry Ellsworth. His second wife was Louisa Hamilton, the
daughter of George and Martha (Harwood) Hamilton and granddaughter of Robert
and Mary (Willison) Hamilton and of John and Polly (Hamilton) Harwood; all of Allegheny County, Maryland. In 1890, Mr. McCurdy located near Dunbar,
PA where on July 15, 1890, two children
(twins) were born to them both dying within a year. George Amelian McCurdy died March 7, 1891. Laurelian Akevia
McCurdy died Oct. 7, 1890.
Mrs.
McCurdy being in poor health, they took a traveling tour thru the mountain
districts of North and South Carolina, Kentucky, Georgia and Tennessee, finally
locating in Rutherford County, NC
[1894] where Mr. McCurdy engaged in farming. In 1897 he removed to
Fayette County, WV. In 1900, they
returned to Maryland and located near the town of Westernport, his present
residence.
In 1902, he engaged with Emil Neffen as clerk
in the hardware trade at Piedmont, WVA, resigning in 1907 to attend to other
pressing duties. They had one child, Maggie Ola, born Dec. 7, 1902.
In the preparation of this history, Mr. McCurdy has shown
rare patience, persistency, and skill in correspondence which has extended over
several years to all parts of the world in gathering the voluminous data herein
presented. The unraveling of genealogical tangles, the ferreting out of correct
lineages and the sifting and arranging of so much information necessitated some
rare qualities of the mind. For all this labor of love, every bearer of the
McCurdy name is truly grateful.
Provided by Betty Latta Kitchen
-- e-mail: Betty Kitchen
Click here for the Short-Cut Table of Contents


switch
statements or expression with default
branches which can never be taken,
because all possible values are covered by a case
branch.
Such elements are redundant, especially for switch
expressions, because they don't compile when all
enum constants are not covered by a case
branch.
The language level needs to be configured to 14 to report switch
expressions.
The provided quick-fix removes default
branches.
Example:
enum E { A, B }
int foo(E e) {
return switch (e) {
case A -> 1;
case B -> 2;
default -> 3;
};
}
After the quick-fix is applied:
enum E { A, B }
int foo(E e) {
return switch (e) {
case A -> 1;
case B -> 2;
};
}
Use the Only report switch expressions option to report only redundant default
branches in switch expressions.


PrePubMed is meant as a stopgap method for finding biology preprints until an institution develops a better method. Although PrePubMed was developed to look and function similarly to PubMed for the familiarity of users, PrePubMed has no affiliation with the NCBI. Any problems with PrePubMed are not reflective of PubMed, or vice versa. I do not know if the PubMed brand is trademarked. No copyright infringement was intended.
PrePubMed is provided "as is". PrePubMed indexes new preprints daily, but indexing preprints is not an exact science, and the daily indexing may encounter an error and as a result the preprints in PrePubMed are not guaranteed to contain the most recent preprints. I try to check to make sure the indexing is running smoothly.
PrePubMed does not store PDFs or the full text of articles. PrePubMed only indexes basic facts such as titles, author names, and abstracts. I believe this indexing falls under fair use. Many other sites index similar data, including PubMed, Google Scholar, OSF Preprints, ScienceOpen, Academic Karma, and papr, and some sites even host entire articles such as ResearchGate.
If you are a copyright holder and believe your copyright is being violated and don't want people to be able to find your article(s), contact me and I will happily delete them from the database.


What is unsupervised learning?
Dimensionality reduction with PCA
Course wrap-up
Deals with a task of inferring latent (hidden) patterns and structures unlabeled data.
The goal is to understand the relationships between features or among observations.
There is only \(X\) and no \(Y\), i.e. there are no special variables such as response or output variables, and no prespecified classes labels for the observations.
Source: ESL Chapter 14
\[C = \frac{1}{n-1} \tilde X ^T \tilde X = \frac{1}{n-1} V \Sigma^2 V^T\]
\[\tilde X = U \Sigma V^T \]
In the above \(U\) and \(V\) are orthogonal matrices and
\(\Sigma\) is a diagonal matrix.
\[T = \tilde{X} V = U\Sigma V^T V = U\Sigma\]
Reduction occurs when the top \(k < p\) components are kept and used to represent the original \(p\)-dimensional data.
The \(k\)-dimensional approximation of \(X\) is:
\[T_k = U_k D_k\]
where \(U_k\) is a matrix with \(k\) first columns of \(U\) and \(D_k\) is the diagonal matrix containing first \(q\) diagonal terms of \(D\)
The built in dataset includes information on violent crime rates in the US in 1975.
head(USArrests)
## Murder Assault UrbanPop Rape
## Alabama 13.2 236 58 21.2
## Alaska 10.0 263 48 44.5
## Arizona 8.1 294 80 31.0
## Arkansas 8.8 190 50 19.5
## California 9.0 276 91 40.6
## Colorado 7.9 204 78 38.7
Mean and standard deviation of the crime rates across all states:
apply(USArrests, 2, mean)
## Murder Assault UrbanPop Rape
## 7.788 170.760 65.540 21.232
apply(USArrests, 2, sd)
## Murder Assault UrbanPop Rape
## 4.355510 83.337661 14.474763 9.366385
prcomp()
can be used to perform PCA.prcomp()
is faster and preferred method over princomp()
; it is a PCA implementation based on SVD.pca.res <- prcomp(USArrests, scale = TRUE)
prcomp()
is a list containing:names(pca.res)
## [1] "sdev" "rotation" "center" "scale" "x"
The elements of prcomp
output are:
head(pca.res$x)
## PC1 PC2 PC3 PC4
## Alabama -0.9756604 1.1220012 -0.43980366 0.154696581
## Alaska -1.9305379 1.0624269 2.01950027 -0.434175454
## Arizona -1.7454429 -0.7384595 0.05423025 -0.826264240
## Arkansas 0.1399989 1.1085423 0.11342217 -0.180973554
## California -2.4986128 -1.5274267 0.59254100 -0.338559240
## Colorado -1.4993407 -0.9776297 1.08400162 0.001450164
These are the sample coordinates in the PCA projection space.
\[L = \frac{V \Sigma}{\sqrt{n-1}}\]
The loadings or principal axes give the weights of the variables in each of the principal components.
pca.res$rotation
## PC1 PC2 PC3 PC4
## Murder -0.5358995 0.4181809 -0.3412327 0.64922780
## Assault -0.5831836 0.1879856 -0.2681484 -0.74340748
## UrbanPop -0.2781909 -0.8728062 -0.3780158 0.13387773
## Rape -0.5434321 -0.1673186 0.8177779 0.08902432
pca.res$rotation
## PC1 PC2 PC3 PC4
## Murder -0.5358995 0.4181809 -0.3412327 0.64922780
## Assault -0.5831836 0.1879856 -0.2681484 -0.74340748
## UrbanPop -0.2781909 -0.8728062 -0.3780158 0.13387773
## Rape -0.5434321 -0.1673186 0.8177779 0.08902432
Assault
, Murder
, and Rape
variables, and a much smaller one on UrbanPop
. Therefore, PC1 measures an overall measure of crime.UrbanPop
. Thus, PC2 measures a level of urbanization.UrbanPop
is independent of the crime rate, and so it is further away on the plot.pca.res$sdev
## [1] 1.5748783 0.9948694 0.5971291 0.4164494
pca.res$center
## Murder Assault UrbanPop Rape
## 7.788 170.760 65.540 21.232
pca.res$scale
## Murder Assault UrbanPop Rape
## 4.355510 83.337661 14.474763 9.366385
A scree plot can be used to choose how many components to retain.
Look for “elbows” in the scree plots
Discard the dimensions with corresponding eigenvalues or equivalently the proportion of variance explained that drop off significantly.
# PCA eigenvalues/variances:
(pr.var <- pca.res$sdev^2)
## [1] 2.4802416 0.9897652 0.3565632 0.1734301
plot(pca.res)
# install.packages("factoextra")
library(factoextra)
# Percent of variance explained:
(pve <- 100*pr.var/sum(pr.var))
## [1] 62.006039 24.744129 8.914080 4.335752
fviz_eig(pca.res) + theme(text = element_text(size = 20))
Each principal component loading and score vector is unique, up to a sign flip. So another software could return this plot instead:
fviz_pca_ind(pca.res) + coord_fixed() +
theme(text = element_text(size = 20))
fviz_pca_var(pca.res) + coord_fixed() +
theme(text = element_text(size = 20))
fviz_contrib(pca.res, choice = "var", axes = 1) +
theme(text = element_text(size = 20))
A biplot allows information on both samples and variables of a data matrix to be displayed at the same time.
fviz_pca_biplot(pca.res) + coord_fixed() +
theme(text = element_text(size = 20))
Go to the “Lec8_Exercises.Rmd” file, which can be downloaded from the class website under the Lecture tab.
Complete Exercise 1.
Clustering is an exploratory technique which can discover hidden groups that are important for understanding the data.
Groupings are determined from the data itself, without any prior knowledge about labels or classes.
There are the clustering methods available; a lot of them have an R implementation available on CRAN.
Source: link
The number clusters \(k\) must be prespecified (before clustering).
The method is stochastic, and involves random initialization of cluster centers.
This means that each time the algorithm is run, the results obtained can be different.
The number of clusters, \(k\), should be chosen using statistics such as:
One of the application of k-means clustering is image segmentation.
Here we use a picture of a field of tulips in the Netherlands downloaded from here.
library(jpeg)
url <- "http://www.infohostels.com/immagini/news/2179.jpg"
dFile <- download.file(url, "./Lecture8-figure/Image.jpg")
img <- readJPEG("./Lecture8-figure/Image.jpg")
(imgDm <- dim(img))
## [1] 480 960 3
The image is a 3D array, so we will convert it to a data frame.
Each row of the data frame should correspond a single pixel.
The columns should include the pixel location (x
and y
), and the pixel intensity in red, green, and blue ( R
, G
, B
).
# Assign RGB channels to data frame
imgRGB <- data.frame(
x = rep(1:imgDm[2], each = imgDm[1]),
y = rep(imgDm[1]:1, imgDm[2]),
R = as.vector(img[,,1]),
G = as.vector(img[,,2]),
B = as.vector(img[,,3])
)
head(imgRGB, 3)
## x y R G B
## 1 1 480 0 0.3686275 0.6980392
## 2 1 479 0 0.3686275 0.6980392
## 3 1 478 0 0.3725490 0.7019608
kmeans()
built-in function.# Set seed since k-means involves a random initialization
set.seed(43658)
k <- 2
kmeans.2clust <- kmeans(imgRGB[, c("R", "G", "B")], centers = k)
names(kmeans.2clust)
## [1] "cluster" "centers" "totss" "withinss"
## [5] "tot.withinss" "betweenss" "size" "iter"
## [9] "ifault"
# k cluster centers
kmeans.2clust$centers
## R G B
## 1 0.5682233 0.3251528 0.1452832
## 2 0.6597320 0.6828609 0.7591578
# The centers correspond to the following colors:
rgb(kmeans.2clust$centers)
## [1] "#915325" "#A8AEC2"
# Cluster assignment of the first 10 pixels
head(kmeans.2clust$cluster, 10)
## [1] 2 2 2 2 2 2 2 2 2 2
# Convert cluster assignment lables to cluster colors
kmeans.2colors <- rgb(kmeans.2clust$centers[kmeans.2clust$cluster, ])
head(kmeans.2colors, 10)
## [1] "#A8AEC2" "#A8AEC2" "#A8AEC2" "#A8AEC2" "#A8AEC2" "#A8AEC2" "#A8AEC2"
## [8] "#A8AEC2" "#A8AEC2" "#A8AEC2"
ggplot(data = imgRGB, aes(x = x, y = y)) +
geom_point(colour = kmeans.2colors) +
labs(title = paste("k-Means Clustering with", k, "clusters (colors)")) +
xlab("x") + ylab("y") + theme_bw()
Now add more colors, by increase the number of clusters to 6:
set.seed(348675)
kmeans.6clust <- kmeans(imgRGB[, c("R", "G", "B")], centers = 6)
kmeans.6colors <- rgb(kmeans.6clust$centers[kmeans.6clust$cluster, ])
Alexander Calder’s mobile
Hierarchical clustering can be performed using agglomerative (bottom-up) or divisive (top-down) approach.
The method requires a choice of a pairwise distance metric and a rule of how to merge or divide clusters.
The output of the method can be represented as a graphical tree-based representation of the data, called a dendogram.
The tree allows you to evaluate where the cutoff for grouping should occur.
Source: ISL
Results for hierarchical clustering differ depending on the choice of:
A distance metric used for pairs of observations, e.g. Euclidean (L2), Manhattan (L1), Jaccard (Binary), etc
The rule used for grouping clusters that are already generated, e.g. single (minimum), completer (maximum), centroid or average cluster linkages.
Different ways to compute dissimilarity between 2 clusters:
head(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
hclust()
performs hierarchical clustering.# We use the Euclidean distance for the dissimilarities between flowers
distMat <- dist(iris[, 3:4])
# We use the "complete" linkage method for computing the cluster distances.
clusters <- hclust(distMat, method = "complete")
plot(clusters, cex = 0.7)
The dendrogram suggests that a reasonable choice of the number of clusters is either 3 or 4.
plot(clusters, cex = 0.7)
abline(a = 2, b = 0, col = "blue")
abline(a = 3, b = 0, col = "blue")
cutree()
function.(clusterCut <- cutree(clusters, 3))
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [36] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 3 2 2 2 3 2 3 3 3 3 2 3 3 2 3 2 3
## [71] 2 3 2 2 3 3 2 2 2 3 3 3 3 2 2 2 2 3 3 3 3 2 3 3 3 3 3 3 3 3 2 2 2 2 2
## [106] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [141] 2 2 2 2 2 2 2 2 2 2
table(clusterCut, iris$Species)
##
## clusterCut setosa versicolor virginica
## 1 50 0 0
## 2 0 21 50
## 3 0 29 0
plot(clusters, labels = clusterCut, cex = 0.9)
rect.hclust(clusters, k = 3, border=c("red", "blue", "green"))
table(clusterCut, iris$Species)
##
## clusterCut setosa versicolor virginica
## 1 50 0 0
## 2 0 21 50
## 3 0 29 0
Try another linkage method like “average” and see if it performs better.
# We use the Euclidean distance for the dissimilarities between flowers
distMat <- dist(iris[, 3:4])
# We use the "complete" linkage method for computing the cluster distances.
clusters <- hclust(distMat, method = "average")
plot(clusters, cex = 0.5)
Here we can choose 3 or 5 clusters:
plot(clusters, cex = 0.6)
abline(a = 1.35, b = 0, col = "blue")
abline(a = 0.9, b = 0, col = "blue")
Again we choose 3 clusters
clusterCut <- cutree(clusters, 3)
table(clusterCut, iris$Species)
##
## clusterCut setosa versicolor virginica
## 1 50 0 0
## 2 0 45 1
## 3 0 5 49
We see that this time the results are better in terms of the cluster assignment agreement with the flower species classification.
plot(clusters, labels = clusterCut, cex = 0.7)
rect.hclust(clusters, k = 3, border=c("red", "blue", "green"))
ggplot(iris, aes(Petal.Length, Petal.Width)) + theme_bw() +
geom_text(aes(label = clusterCut), vjust = -1) +
geom_point(aes(color = Species)) + coord_fixed(1.5)
Go to the “Lec8_Exercises.Rmd” file, which can be downloaded from the class website under the Lecture tab.
Complete Exercise 2.
Where to find out more about the topics of this class:
How to learn more advanced topics on R:
MDS algorithm aims to place each object in N-dimensional space such that the between-object distances are preserved as well as possible. Each object is then assigned coordinates in each of the N dimensions. The number of dimensions of an MDS plot N can exceed 2 and is specified a priori. Choosing N=2 optimizes the object locations for a two-dimensional scatterplot.
There are different types of MDS methods including, Classical MDS, Metric MDS and Non-metric MDS. The details on the differences ca be found on:
14 colors were studied with wavelengths in the range between 434 and 674 nm.
# color similarity scores
ekmanSim <- readRDS("./Lecture8-figure/ekman.rds")
print(ekmanSim)
## 434 445 465 472 490 504 537 555 584 600 610 628 651
## 445 0.86
## 465 0.42 0.50
## 472 0.42 0.44 0.81
## 490 0.18 0.22 0.47 0.54
## 504 0.06 0.09 0.17 0.25 0.61
## 537 0.07 0.07 0.10 0.10 0.31 0.62
## 555 0.04 0.07 0.08 0.09 0.26 0.45 0.73
## 584 0.02 0.02 0.02 0.02 0.07 0.14 0.22 0.33
## 600 0.07 0.04 0.01 0.01 0.02 0.08 0.14 0.19 0.58
## 610 0.09 0.07 0.02 0.00 0.02 0.02 0.05 0.04 0.37 0.74
## 628 0.12 0.11 0.01 0.01 0.01 0.02 0.02 0.03 0.27 0.50 0.76
## 651 0.13 0.13 0.05 0.02 0.02 0.02 0.02 0.02 0.20 0.41 0.62 0.85
## 674 0.16 0.14 0.03 0.04 0.00 0.01 0.00 0.02 0.23 0.28 0.55 0.68 0.76
# convert similarities to dissimilarities
ekmanDist <- 1 - ekmanSim
cmdscale()
built-in function for classical MDS.smacof
and other packages are also compared here.ekmanMDS <- cmdscale(ekmanDist, k = 2)
res <- data.frame(ekmanMDS)
head(res)
## X1 X2
## 434 -0.2137161 -0.41852576
## 445 -0.2562012 -0.41065436
## 465 -0.4119890 -0.30925977
## 472 -0.4369586 -0.27266935
## 490 -0.4388604 0.07518594
## 504 -0.3364868 0.37262279
library("ggplot2")
wavelengths <- round(seq( 434, 674, length.out = 14))
res$wavelength <- factor(wavelengths, levels =wavelengths)
ggplot(res, aes(X1, X2)) + geom_point() + theme_bw() +
geom_text(aes(label = wavelength), vjust=-1)
The wavelengths were converted to hexadecimal colors using this website.
hex <- c("#2800ff", "#0051ff", "#00aeff", "#00fbff", "#00ff28", "#4eff00", "#92ff00",
"#ccff00", "#fff900", "#ffbe00", "#ff7b00", "#ff3000", "#ff0000", "#ff0000")
ggplot(res, aes(X1, X2)) + theme_bw() +
geom_point(aes(color = wavelength), size = 2) +
geom_text(aes(label = wavelength), vjust=-1) +
scale_color_manual(values = hex)
The following example shows how to calculate and plot a 2D t-SNE projection using the Rtsne
package. The example and code was developed by Lukas Weber.
# here we use a subset of the data
path <- "./Lecture8-figure/healthyHumanBoneMarrow_small.csv"
dat <- read.csv(path)
# We select 13 protein markers to used in Amir et al. 2013
colnames_proj <- colnames(dat)[c(11, 23, 10, 16, 7, 22, 14, 28, 12, 6, 8, 13, 30)]
dat <- dat[, colnames_proj]
head(dat)
## X144.CD11b X160.CD123 X142.CD19 X147.CD20 X<IP_ADDRESS>.CD3
## 1 5.967343 14.0255518 0.5294468 5.0397625 149.204117
## 2 -2.965949 -0.4499034 -0.9504946 3.2883098 102.398453
## 3 22.475813 7.9440827 -2.5556924 -0.3310032 -9.759324
## 4 -5.457655 -0.3668855 -0.8048915 1.7649024 146.526154
## 5 127.534332 13.2033119 0.7140800 -1.0700325 7.266849
## 6 12.181891 9.0580482 1.9163597 2.1253521 653.283997
## X158.CD33 X148.CD34 X167.CD38 X145.CD4 X115.CD45 X139.CD45RA
## 1 2.4958646 4.3011222 29.566343 0.8041515 606.56268 291.058655
## 2 0.3570583 1.3665982 26.355003 -0.2354967 192.41901 -1.998943
## 3<PHONE_NUMBER> 3.0677378 165.949097 0.3407812 98.22443 5.670944
## 4 -2.2423408 -0.7205721 3.933757 -0.6418993 482.09525 13.697150
## 5<PHONE_NUMBER> -0.9823112 193.646225 30.6597385 212.06926 6.608723
## 6 3.9792464 -1.5659959 163.225845<PHONE_NUMBER> 284.07599 36.927834
## X146.CD8 X170.CD90
## 1<PHONE_NUMBER> 12.444887
## 2 35.8152542 -0.615051
## 3 0.8252113 13.740484
## 4<PHONE_NUMBER> 8.284868
## 5 2.1295056 10.848905
## 6 14.1040640 6.430328
# arcsinh transformation
# (see Amir et al. 2013, Online Methods, "Processing of mass cytometry data")
asinh_scale <- 5
dat <- asinh(dat / asinh_scale)
# prepare data for Rtsne
dat <- dat[!duplicated(dat), ] # remove rows containing duplicate values within rounding
dim(dat)
## [1] 999 13
library(Rtsne)
# run Rtsne (Barnes-Hut-SNE algorithm) without PCA step
# (see Amir et al. 2013, Online Methods, "viSNE analysis")
set.seed(123)
rtsne_out <- Rtsne(as.matrix(dat), perplexity = 20,
pca = FALSE, verbose = FALSE)
# plot 2D t-SNE projection
plot(rtsne_out$Y, asp = 1, pch = 20, col = "blue",
cex = 0.75, cex.axis = 1.25, cex.lab = 1.25, cex.main = 1.5,
xlab = "t-SNE dimension 1", ylab = "t-SNE dimension 2",
main = "2D t-SNE projection")


Previously released versions of the Legato online help are stored on GitHub in the related directory.
The default version accessed from Legato.io is the most recent Legato docs.
To access previous versions:
Copyright (C) Sierra Wireless Inc. Use of this work is subject to license.


Open the session to the IMAP server
telnet mail.yourdomain.com 143
Send the username and password to the system (the "a" is a required part of the command)
a LOGIN <user> <pass>
Shows a listing of all the IMAP folders the account has
a LIST "" "*"
Select the INBOX folder. You can repalce INBOX with any folder you wish to use
a SELECT INBOX
List all the emails in the selected folder, and will also tell you the flags like "seen" or "NonJunk"
a fetch 1:* flags
List the header information of message 1 in the selected folder
a fetch 1 body[header]
List the header AND the contents of message 1 in the selected folder
a fetch 1 body[]
Set message 1 to be deleted
It will not delete until the expunge command has been run
a store 1 +flags deleted
Delete any emails that you flagged to be deleted with the previous command
a expunge
Disconnect from the IMAP server
a logout


--- layout: default ---
Multi-talented web UX/UI designer and developer, Envato Elite author with over 12000 awesome customers and the creator of the best selling WordPress knowledge base plugin.
Passionate about creating beautiful and useful websites since 2011, specializing in WordPress and Jekyll development.


|
The PrisonerThe poignant thing about innocence is that you can only lose it once.Starting in 1967, millions of viewers lost their innocence to a British television production known as The Prisoner. Unlike anything previously seen in mainstream television or film, The Prisoner permanently changed the rules of engagement between makers of entertainment and consumers of entertainment. Before The Prisoner, you could settle down in front of the TV secure in the knowledge that you would be served up a tasty and reasonably satisfying bit of fluff with a beginning, a middle and an end. While the quality might vary, depending on the skills of the writers, directors and actors involved, you knew for certain that everyone was trying their best to give you what you wanted. Into that complacency, The Prisoner dropped like an atomic bomb. Over the course of 17 one-hour episodes, The Prisoner toyed with its viewers like a particularly sadistic cat with an especially clueless mouse. The show trotted out its tantalizing enigmas with the usual straight-faced sobriety, challenging viewers to figure out the Big Questions before the big finale in which all would be revealed. The viewers took up that challenge with childlike enthusiasm, little dreaming what Daddy had in store for them. The Prisoner was the brainchild of Patrick McGoohan, who had become familiar to British and U.S. viewers as John Drake, the stalwart protagonist of Danger Man (aka Secret Agent). Danger Man was a cross between The Saint and Mission Impossible, a fairly straightforward spy show in which Drake traveled the globe, performing acts of espionage and derring-do. With its brisk action and memorable theme song (Secret Agent Man by Johnny Rivers), Danger Man was extremely popular, but McGoohan was getting bored with its black-and-white morality tales and formulaic plots. McGoohan was approached to play James Bond, but he declined, fatefully suggesting Sean Connery for the role instead. ITV, the production company behind Danger Man was keen to pick the series up for another season, but McGoohan made a counterproposal. He had an idea for a new series in the spy genre, a series he would produce, write and direct. The concept was easy enough to comprehend at this stage. A secret agent resigns from his employer in the midst of the Cold War and is subsequently kidnapped by forces unknown. His captors take him to a strange town simply known as The Village. Located on a remote island, The Village seems like a quaint anachronism of a British resort town, but every resident is subjected to constant surveillance by hidden cameras and spies planted among the population. McGoohan's character is of particular interest to his captors. They wanted to know why he resigned, and they played endless mind games with their prisoner in an effort to get him to talk. While the idea itself was fairly simple, its execution was unbelievably complex. Everyone in the Village is given numbers. Names are never used, a rule observed so assiduously that the audience never even learns the name of the main character, played by McGoohan. Characters were assigned numbers. The Prisoner was told that he was No. 6, but he refused to accept the designation, defiantly shouting, "I am not a number, I am a free man!" The Village was a trippy place. Security was provided by Rover, a menacing white blob portrayed by a weather balloon with a penchant for smothering wayward prisoners. Penny-farthing bicycles were everywhere in the Village, inexplicably. The technology was pure science-fiction for its day. Although the cordless phones, tape-spooling computers, lava lamps and miniature cameras are painfully dated today, they were quite the space-age artifacts at the time. Fortunately, the surreal nature of the Village mostly masks the show's 1960s sensibilities for modern viewers. The Village was run on a day-to-day basis by No. 2. Each week, there was a new No. 2, played by a different actor, with little explanation offered. No. 2 had many responsibilities, but one towered over all the rest... Find out why No. 6 resigned. Each week brought new and elaborate mind games designed either to extract this ultimate secret, or simply to break the Prisoner's spirit. Each week, the effort failed, but it was a close call sometimes. Viewers entered the series as clueless about the reasons for the Prisoner's resignation as his captors. Several other major questions immediately presented themselves: Which side runs the Village? Is it "us" or "them"? What do they really want? And perhaps most important, if No. 2 is the administrator of the Village, who is No. 1? Rather than simply wire electrodes to the Prisoner's genitals, each No. 2 presented a new and innovative scheme to unlock the Prisoner's brain. Ploys ranged from hallucinogen-induced alternate realities to fixed elections to staged escapes to femme fatales to identical twins to brain surgery. Some were so bizarre they can barely be related, others were elegant in their simplicity. Almost every episode was an allegory about the inherent struggle of maintaining individuality while living in society. Sometimes the struggle was buried in dense layers of symbolism, as in "The Dance of the Dead." Other times, the theme was profoundly in your face. In the episode titled "Free for All," the Prisoner is invited to run for the position of No. 2 in a scathing indictment of the democratic process and the media. An interview with the candidate goes to press before it's even finished. "Less work and more play" is a platform plank. Under the influence of brainwashing, the Prisoner spouts meaningless gibberish to cheering crowds. The episode "Living in Harmony" featured a typical Prisoner episode, only set in the American Old West. The Prisoner is forced to serve as the sheriff of a small town called Harmony. The Sheriff refuses to carry a gun, which was considered a loaded political comment during the height of the Vietnam War, and the episode was censored by CBS the first time the series ran in the United States. McGoohan wrote and directed several of the episodes, including the final two episodes, which represent the culmination of the series. Other collaborators included series co-creator George Markstein (who left the show in a huff when he found out what McGoohan had planned for the finale), David Tomblin, Anthony Skene and Vincent Tilsley. It's probably best not to discuss the plot of the final two episodes of The Prisoner, which are best experienced without spoilers. But it's safe to say that the final episode was easily the most controversial hour of television in history. While many viewers were still uncertain about the answers to their questions, they had fairly clear expectations about what constituted answers, and they felt entitled to receive some. You can mount an argument about whether The Prisoner answered any or all of its many questions in the final episode, but it definitely wasn't like anything that had been seen on television before. Angry calls poured into the switchboards at ITV for hours after the program ended. Ten years later, an interviewer asked McGoohan the question on everyone's mind. What was he thinking?  Why don't you do more (shows) that we can understand?' I was delighted with that reaction. I think it's a very good one; that was the intention of the exercise." Whatever one chooses to take from the final episode of The Prisoner, the series was a tremendous influence on movies and television that came after it. Its cool paranoia, intelligent characters and razor-sharp dialogue directly influenced dozens of concepts that came after, including The X-Files and Twin Peaks. In addition to stylistic and thematic influences, The Prisoner raised the possibility of a new kind of art disguised as entertainment. Just two years later, 2001: A Space Odyssey would offer a similar kind of conclusion, a climax deeply masked in symbolism and considered impenetrable by some. The lack of closure in the final episode of The Prisoner opened the door to Surrealism and abstraction in television, a medium that had previously been distinguished mostly by the depressing sameness of its fare. McGoohan offered up the visual equivalent of a Salvador Dali painting: difficult, disturbing and nonlinear. A Prisoner comic book sequel was published in the late 1990s, and a handful of books and parodies occasionally pop up. In a 2000 episode of The Simpsons, McGoohan reprised the role of the Prisoner opposite the redoubtable Homer Simpson. When Homer's Web site accidentally uncovers a government conspiracy, he's kidnapped and taken to "The Island" where he's given the number 5. "In your face, Number Six!" he crows. In the end, the whole Simpson clan is taken to The Island, apparently for life. As Marge observes, "Once you get used to the constant druggings this place really isn't so bad." Rumors of a Prisoner movie have circulated for years, but the project never seems to get off the ground. If it happens, it will almost certainly suck, no matter how skillfully it is crafted. Because, don't forget, the poignant thing about innocence is that you can only lose it once.
|


Features.
Raw West
We are not tailoring your experience but trying to show life as real cowboys. You will have the most authentic and undecorated western experiences.
Airbnb Compatible
We have a cozy guest room that can host up to two guests, equipped with microwave, mini-fridge, fast WI-FI and hot shower. The rating on Airbnb is 4.8+.
Easy Access
The location is right between Zion national park, Bryce national park, lake Powell and Grand Canyon north rim. It is a base for your exploraton in Arizona and Utah.
Sailor!
Sailor is our dog. He is a super playful and friendly Austrilian shepherd. He loves playing throw and fetch. If you travel alone, Sailor is an awesome companion.


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Everyone has their own dreams, I am the same. But my dream is not a lawyer, not a doctor, not actors, not even an industry. Perhaps my dream big people will find it ridiculous, but this has been my pursuit! My dream is to want to have a folk life! I want it to become a beautiful painting, it is not only sharp colors, but also the colors are bleak, I do not rule out the painting is part of the black, but I will treasure these bleak colors! Not yet, how about, a colorful painting, if not bleak, add color, how can it more prominent American? Life is like painting, painting the bright red color represents life beautiful happy moments. Painting a bleak color represents life difficult, unpleasant time. You may find a flat with a beautiful road is not very good yet, but I do not think it will. If a person lives flat then what is the point? Life is only a short few decades, I want it to go Finally, Each memory is a solid.


Components¶
The real digital magic in the machine happens in a variety of very simple
parts. Each part has a set of input signals, and a set of output signal
connections. We will provide a tick
method that reads the inputs, performs
some processing, then delivers the results to the appropriate outpit points.
And the initial test case:
The Component class is not used directly in our simulator. Instread, it os a
base class from which all uniwue parts are derived. The component provided the
basic structure of a part. Only the tick
method needs to be customized in
the actual part classes.


Welcome to Zelious. These are the terms and conditions governing your access to and use of the website Zelious.net By accessing, browsing, and using our website or any of our applications through whatever platform and/or by placing your order, you acknowledge and agree to have read, understood, and agreed to the terms and conditions set out below
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Your National ID should be Valid.
The name you register with on your Zelious account should match your name on your national ID, to avoid any issues at customs clearance. Changing the name of the airway bill after the process of shipping will cost you 500 EGP.
We will only be responsible for the delay if you already uploaded your ID when it was requested.
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Zelious's service level starts when the shipment arrives at one of our warehouses, the period before we receive the order(s) at any of our warehouses depends on the seller, not Zelious.
- Zelious is not responsible for any delay may cause from the seller side while shipping your order to one of our warehouses.
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- In case you request a return, we will contact the seller upon your request regarding product exchanges or return. we will arrange for the return only if approved by the seller. Also, we will apply the applicable return charges (Depends on the item's weight or Dimension and the seller's return charges).
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You chose edfa3ly to follow all import, export, and customs clearance operations and to pay all customs dues and taxes to the competent tax and customs authorities.


--- layout: post title: Pyr Pressure categories: - lifestyle ---
It's a shame I do not do lifestyle posts more often, but with the new blog I hope to change that. To that end, this article is dedicated to our Great Pyrenees Sasha who passed away from bone cancer last month. She was just 7 years old and we only had 3 weeks of good times from the point she was diagnosed with osteosarcoma, a form of bone cancer. I am sure everyone loves their pet(s), we even have another dog and 3 cats. But there is something about a large breed, perhaps more so a Great Pyrenees, that made our loss really painful. It was also the first time I heard about the rainbow bridge, total tear buster.
Luckily for us, we have a personal friend that breeds Great Pyrenees show dogs in Smithfield, VA and she just so happens to have had a new litter. Determined to turn a bad situation into something positive, we just up'ed our family by two more puppy dogs. You could say I RAID-1'ed my pyr puppies. So we now have a little boy and girl from the same litter. His name is Remy LeBeau and is on the left and her name is Sonya, pictured on the right.
They are getting tons of love, attention, and good food. Raising two puppy dogs is so much fun, but it takes a lot of time and patience, especially when training them to go out doors with all the snow we have gotten lately. In the future Kelly and I will enjoy walking these guys around Colonial Williamsburg. Since they look like sheep, our Border Collie will corral them indoors for us as he did for Sasha. We can already tell that Remy will be the protector of the household and probably weigh in around 150 pounds when he grows up.


Gardaí appeal for help in locating 17-year-old from Mullingar
by By Digital Desk staffGardaí are appealing for help in locating a missing 17-year-old from Mullingar.
Janelle Quinn has been missing since Firday, November 1.
Janelle is described as being 5'4' with a broad build and long brown hair.
Anyone with information or anyone who can assist the Gardaí in locating Janelle is asked to contact Gardaí in Mullingar on<PHONE_NUMBER> the Garda confidential Line on 1800 666 111 or any garda station.
Earlier today, gardaí issued an appeal for help in locating a man missing from Galway.
READ MORE
Lunney's brother says it was 'very difficult' to see Kevin recount ‘brutally specific’ torture


Paper Wallet for all Crypto Coins
This tool helps you to quickly create a nicely formatted paper wallet with auto-generated QR-codes.
- Disconnect your computer from the internet
- Generate your public and private addresses on another website or with another tool
- Enter the name of the cryptocoin in the top-left field
- Enter the public information in the left side.
You can enter 1 or 2 public strings and specify the name of that key.
Usually the title will be "Address" and you can enter the public address in the field below.
A QR-code is auto-created.
- Enter the private information on the right side.
Enter the type of key name like: "Private Key" and the actual key below that.
You can enter 3 different keys/secrets/etc.
- Click the print link at the top-left to auto-remove unused fields and print on an A4/US letter in landscape.
- Cut away the parts you don't need and fold the private key over the middle section
Ok, got it


Functionality is added to mission-control through plugins and its Plugin API.
Plugins expose a single function that gets called dynamically by mission-control.
Passed during initialization is a PluginContext
that exposes mission-controls features to the plugin.
PluginContext
exposes access to:
Start by creating a new file


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To reunited them, you have to search and find out the twins that look exactly alike


Urban Grammar AI
research project
tobler
Have you ever needed to link two sources of data, each attached to a different geometry? In our work in the WP2, we do. We have to transfer data from various sources, linked to output areas, urban blocks or other spatial units to our own bespoke set of geographies. Therefore, we often need to do areal interpolation to correctly map data from one layer to another. Luckily, the open-source Python ecosystem can help.
Tobler, a part of PySAL family, is a library for areal interpolation and dasymetric mapping which already offered what we needed. However, our data tends to be large, up to 15 million rows on which we need to interpolate several hundreds of thousands of rows of input data. That can take a while, so each performance improvement can help a lot.
We have looked into the existing code of tobler and contributed to the refactoring if its area_interpolate function. The original implementation was using custom code for spatial indexing, which was replaced by a performant vectorised implementation based on the more recent pygeos project.
area_interpolate
pygeos
With sample data, we’ve been able to speed up the interpolation from 2.4 seconds to less than 400 milliseconds, having the same result 6x faster.
Such an improvement is great, but it still uses only a single core (as most of the geospatial code to be honest), leaving the rest in a modern computer (four or more cores is not uncommon these days…) lazily laying around. We have tried to change this and contributed a (still experimental) parallel implementation of the same algorithm (based on joblib).
joblib
The resulting improvements depend a lot on the input data structure, and we are still working of further tweaking, but certain combinations can provide significant speedups, which can later save us some time.
Interpolation is not always the best way of transferring data from one layer to the other, especially for categorical variables. We have found ourselves in need of a spatial join based on the largest intersection between geometries - the category from the largest intersection gets joined to the target geometry. Such a function was not available (at least we have not found one) so we have developed it. Once it was done and battle-tested on our application, we have contributed it as a new addition to tobler. Everyone can now use it as area_join.
area_join
## Full list of contributions
The complete list of pull requests implementing code resulting from the Urban Grammar AI project is below:
use STRtree.query_bulk in _area_tables_binning #110
STRTree parallel implementation #112
add area_join for join based on the largest intersection #119
fix parallel binning #123


I'm a UXer with an interest in learning to code, because I love seeing the tangible results of my work! When I've learned some more, I'll be a pretty pretty unicorn.
I can support a project throught an interative cycle by:
Drop me a line at email@email.com


Role: Product Designer | Product Manager | Product Owner
Team Size: 5 Date: 2014/12-2015/01
Category: Information Architecture Product DesignStartup Project
Easi Game App is a side project for 360 PC Security's Weekly Game section which has over 600k active users and convert 100k to 360 Game Center, one of the company's most profitable product.
As more users migrate to wireless devices, the significance of PC platform is decaying. It is hard to maintain the current active users, and our team need to find new opportunity to create more value.
Create a HTML 5 game platform that transform the current 600k users on PC to mobile app.
For this project, I was the sole product designer on the team and was responsible for designing the app and the content management system from scratch. Since this project is treated as an internal startup project, I also served the role of product owner that thrives to pull resources to support the development and push the promotion of this platform.
I worked with an UI designer, an android developer, a game developer, and a database developer to make the app and games in it go live.
In 2014, one HTML5 game called "Trap the Crazy Cat" has gone viral in China. By leveraging one of China's biggest social network, Wechat Moment, it has accrued 200,000,000 pageviews in merely 3 days.
By conducting case study on this game, we noticed that although the game is very mundane, there seems to be a formula that works:
This type of games are easy to share and play since it requires no installation
When a round of game ends, it assign scores that advocates social comparison
The game can be finished in a few minutes, thus not costing lots of productivity
There's no specific technique, which encourages people to try again and again
We hope that by learning from this there could be a way to make our product and go viral too.
By assessing current market environment, we also noticed that existing HTML5 games present lots of territory unexplored, despite its advantage in being able to distribute through multiple channels.
All the major competitors are taking similar approach in feature and design, with no apparent differentiation and share some common issues:
Stuffing all existing games into the app.
No champion board or any user focused features.
Games themselves are low quality
For the PC version of weekly games, our team worked with the game development team to curate one well made flash game every week. From the data gathered for the PC version, I found that the daily ranking system and the starting screen of the game have significant effects on engagement.
When we first kick start the project, we can only get license for games we self-developed, so we worked with our company's game team to develop several games for the initial launch.
Based on the data of pc version, we decided to transform some of the most well received flash games to html5 games.
I started designing the app through exploring the user flow, from where it start to ending game, getting on chart and sharing with others
In the first version, we also investigated on various ways to present the game list.
We tried to utilize the high quality game by presenting the game to users right upon entrance.The rest of the games were present in a picture flow form to encourage engagement. Champion chart and sharing feature are placed at the top navigation bar to encourage user usage.
This project was conducted under a very agile process and the first version was immediately launched after development to gather user feedback data. Through looking at the data collected we realized that:
The champion chart is rarely looked at.
Share function was almost never used after gaming.
Multiple feedbacks complaining there's too few games to play with.
After finding out that features like sharing and champion chart that we want to utilize is not properly emphasized. We started reconsidering the user flow and realized that these two features being placed at the top left corner makes it detached from the whole gaming process. So we decided to redesign the user flow to better fit user context.
While doing the first version, we utilized a pinterest like layout to highlight the quality of the few games that we have available. However, as our licensed game grows, we decided to switch to version 2 to allow users to explore the amount of games we have.
Through the test run, we found out that even though the weekly game was well received, it still didn’t escape the short life cycle nature. On top of that, the loading time of the featured game also result in a high bounce rate right upon entrance. Thus, we decided to get rid of the three tabs and the featured games, but just focus on games available
Through the test run and user feedback, we realized that placing sharing and champion chart on the navbar disassociated them with the game results.So we decided to place them on the game end screen to emphasize its relation to scores
The shift from test run version to version 2 has resulted in significant increase in new account and people checking out the champion chart and replaying the game. Version 2 received 30% more daily downloads and maintained over 10% daily active rate


When investigating a given system's trajectories and dynamics it
is often not intuitive how the system generally behaves. Even after
solving the state space and finding the eigenvalues and eigenvectors that
give information about the stability, it is still hard to visualize what
is occurring for a given set of trajectories.
In the special case of second-order systems, a phase portrait is particularly
useful. It plots the system trajectories as contours over a timespan along the X and Y axes
which respectively represent the first and second order of the state space. In the
case of nonlinear systems, phase portraits are particularly useful
because they do no require any linearization or elimination of nonlinear terms.
The phase portraits is able to perfectly capture all of the nonlinear trajectories
and display them in a way that would be otherwise difficult.
The simple pendulum is a great example of a second-order nonlinear system that can
be easily visualized by the phase portrait. The simple pendulum is often linearized using
the small-angle approximation and converted into a linear form of zdot = A*z
where
A
is a matrix of scalars. Instead, the phase portrait method
leaves the simple pendulum state space in its nonlinear form as sin(θ)
.
It is worth noting that the inherent limitation of phase portraits is that they only
applicable in second-order or lower systems. However, it cannot be asserted
enough how effective phase portraits are at giving quick analysis for even
highly-nonlinear systems.
The code below provide phase portraits for various dampened and undampened
simple pendulum scenarios. Notice how the dampened trajectories converge
to a point while the undampened ones form concentric contours, formally known as
limit cycles.
Also, notice how for the larger dampening coefficient, the trajectories converge to a point
much quicker, with fewer rotations around. This reflects our intution that a
pendulum with more friction (a larger dampening coefficient) will come to rest faster.
Similarly, the limit cylces formed
in the undampened figures show that without any friction, the pendulum will continue
to swing around the equilibrium point forever. It is clear why phase portraits
are a powerful tool for analyzing nonlinear systems, read more about
them here.
% Script for Plotting the Phase Portrait of a Nonlinear Pendulum
% init system and states
g = 9.81;
L = 1;
ss = @(t,THETA) [THETA(2); -g./L*sin(THETA(1))];
THETA1 = linspace(-2*pi,2*pi,50);
THETA2 = linspace(-2*pi,2*pi,25);
% Generate Meshgrid for numerically solving
[x,y] = meshgrid(THETA1,THETA2);
size(x)
size(y)
% Establish U and V vectors
u = zeros(size(x));
v = zeros(size(y));
% Init Loop over Vector field to compute Derivatives
t = 0;
for i = 1:numel(x)
Theta_d = ss(t,[x(i); y(i)]);
u(i) = Theta_d(1);
v(i) = Theta_d(2);
Mag = sqrt(u(i)^2+v(i)^2);
%u(i) = u(i)/Mag;
%v(i) = v(i)/Mag;
end
%Plot our vector field
quiver(x,y,u,v,'r'); figure(gcf)
xlabel('${\theta}$','interpreter','latex')
ylabel('$\dot{\theta}$','interpreter','latex')
axis tight equal;
hold on
% Plotting Solutions for Comparison
for THETA_sol = linspace(-4*pi,4*pi,150)
[ts,ys] = ode45(ss,[0,50],[0;THETA_sol]);
plot(ys(:,1),ys(:,2),'b')
plot(-ys(:,1),ys(:,2),'b')
%plot(ys(1,1),ys(1,2),'bo') %start of contour
%plot(ys(end,1),ys(end,2),'ks') %end of contour
end
axis([-5 5 -2*pi 2*pi])
grid on
hold off
% Script for Plotting the Phase Portrait of a Dampened Nonlinear Pendulum
% Change the state space to include "b" dampening coefficient
% init system and states
g = 9.81;
L = 1;
b = 1;
ss = @(t,THETA) [THETA(2); -b.*THETA(2)-g./L*sin(THETA(1))];
THETA1 = linspace(-2*pi,2*pi,50);
THETA2 = linspace(-2*pi,2*pi,25);


C# IEnumerable and Yield
I have a class which contains two lists of strings. I want to iterate over both lists at the same time getting pairs of values. The neat way to do this is to make the class enumerable, which means it can be used in a foreach loop.
There are three things to do:
- The class needs to inherit from the
IEnumerable<T>
interface. - The class needs to implement the method
IEnumerator<T> GetEnumerator()
. This method returns anIEnumerator<T>
, so we need one of those. C# has the yield keyword which lets you do this easily. - Because I want to return two values from my class, I can't return a simple type like string. Instead I need a class or struct to put the pairs of values in.
The collection class implements IEnumerable
, which indicates it can be iterated over. The IEnumerator
is the thing which does the iteration. You, the programmer, never actually see the IEnumerator
; it's hidden behind the the foreach construct.
using System.Collections;
using System.Collections.Generic;
using MbUnit.Framework;
namespace EnumeratorDemo
{
[TestFixture]
public class EnumeratorTest
{
public struct Element
{
private string propertyName;
private string legendString;
public string PropertyName { get { return propertyName; } }
public string LegendString { get { return legendString; } }
public Element(string propertyName, string legendString)
{
this.propertyName = propertyName;
this.legendString = legendString;
}
}
public class Series : IEnumerable<Element>
{
private List<string> propertyNames;
private List<string> legendStrings;
public Series(List<string> propertyNames, List<string> legendStrings)
{
this.propertyNames = propertyNames;
this.legendStrings = legendStrings;
}
public IEnumerator<Element> GetEnumerator()
{
int i = 0;
foreach (string propertyName in propertyNames)
{
yield return new Element(propertyName, i < legendStrings.Count ? legendStrings[i] : string.Empty);
++i;
}
}
IEnumerator IEnumerable.GetEnumerator()
{
return this.GetEnumerator();
}
}
[Test]
public void Test()
{
List<Element> series = new List<Element>();
foreach (Element element in new Series(new List<string>(){"prop1", "prop2"},
new List<string>(){"leg1"}))
{
series.Add(element);
}
Assert.AreEqual(2, series.Count);
Assert.Contains(series, new Element("prop1", "leg1"));
Assert.Contains(series, new Element("prop2", string.Empty));
}
}
}
I spent a while with the following rather annoying compile error:
'EnumeratorDemo.EnumeratorTest.Series' does not implement interface member 'System.Collections.IEnumerable.GetEnumerator()'. 'EnumeratorDemo.EnumeratorTest.Series.GetEnumerator()' cannot implement 'System.Collections.IEnumerable.GetEnumerator()' because it does not have the matching return type of 'System.Collections.IEnumerator'.
After a bit of googling I found out this is because I was not implementing the non-generic IEnumerator
interface, which the generic IEnumerator<T>
inherits from. Hence the extra code in the class implementing the non-generic IEnumerator IEnumerable.GetEnumerator()
method.
In the real application the Series class is not so trivial. It's constructor takes two strings as arguments, which contain comma separated values. The Series class parses the comma separated values to make the two lists.


☢
Aw, Snap!
He's Dead, Jim!
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